There is a growing need for the real-time detection and classification of airborne biological and non-biological particles for indoor and outdoor air quality monitoring, as well as, for the early detection of deliberate releases of biological agent aerosols on the battlefield and in urban environments, such as through a terrorist act. Airborne microorganisms can cause diseases and the real-time monitoring of hospitals, manufacturing operations, sewage plants, animal production houses, and recycling or composting plants can help prevent harmful exposure of microorganisms in these environments. Further detection of particle-sized impurities can benefit quality and production, for example, in chip manufacturing processes. There is a further need to monitor the exposure of humans to organic carbon particulates in urban environments. The majority of organic carbon particulates encountered in the environment from such sources as diesel emissions and burning vegetation contain polycyclic aromatic hydrocarbons which are carcinogenic to humans.
The ability to provide a real-time warning of a bio-aerosol attack is a challenging problem. Present state-of-the-art real-time biological point detection involves sensing the auto-fluorescence of biological particulates via the excitation of endogenous fluorophores and by measuring the elastic scattering of particles. There are two primary limitations of the present art. First, is the inability to sense in a reliable manner low levels of cellular and spore type particles in singlet form and protein toxin and viral aggregates that fall below a stated level, e.g. a couple of microns. Second, is the inability to classify biological particles in a manner that produces a low false alarm rate when set for a threat level that corresponds to a low-level attack.
The recent delivery of parcels containing weapons-grade Anthrax, or other biological particles, and the release of these spores into the U.S. postal system demonstrated a spore-type threat delivered primarily in singlet form. Other potential attacks related to terrorist activity could be the release of biological agents into public areas, facilities and government complexes. The dispersal methods employed would determine in what form the biological agent would be packaged. In other words, the dispersal methods employed would determine what size aggregate was generated, or if single cellular or spore-type agents were generated.
For example, with a crop duster or portable crop sprayer, one could assume that a respirable range of aggregates larger than a two to ten (2–10) micron in diameter would be the predominant size generated primarily because of the water droplet diameter that these types of atomizers produce. However, for a covert release in a facility or public area, one could expect a dry powder release or a low output nebulizer could be used that would generate cellular and spore-type agents in single form or viral/protein toxin aggregates that are below 1 micron in size.
U.S. Patent Application Publication No. U.S. 2003/0098422 A1 discloses a method and apparatus for biological particle detection and classification using Mie scattering techniques and auto-fluorescence. Such Application is incorporated by reference in its entirety as if made a part of this present application.
In preparing for all threat scenarios, the ability to detect small viral/protein toxin aggregates and the singlet form of cellular and spore-type agents is required, in addition to, the conventional respirable range aggregate (2–10 micron). A further requirement is the ability to classify biological agents of interest and to separate them from commonly encountered biological particulates such as mold spores, pollens, and other biological cells and spores, as well as, other types of commonly encountered aerosols such as diesel soot and inorganic/organic particulates. Efforts directed at classification of most types of aerosols commonly encountered, as well as the biological agents of interest, will have a direct impact on the false alarm rate of real-time biological agent detection.